DataTechnology Stack
Practical tooling. Pragmatic choices. Open by default.
We focus deeply on a proven toolkit rather than chasing every new tool, each technology below is something we actively build with.
Cloud Platforms
Multi-cloud capable, with experience across the major providers
Data Platforms
Modern lakehouse and warehouse architectures with open formats.
Data Engineering
Streaming + batch unified, with built-in observability.
Programming
Fluent in the core languages of modern data and platform engineering.
Infrastructure
Versioned infrastructure with policy-as-code and GitOps delivery.
ML & AI
From training pipelines to feature stores to LLM-powered applications.
Observability
Visibility into systems and data quality, end to end
Security & Compliance
Zero-trust foundations and audit-ready controls.
Four rules we keep coming back to
Tooling is a long-term cost. We optimise for what will still make sense in three years, not this quarter's hype cycle.
Proven foundations
Use proven tech where you can
Postgres, Airflow, Terraform — proven, debuggable,, easy to hire for.
Pick newer tech where it earns its place
Iceberg, Flink, Rust — when the problem genuinely calls for it, not just because it's trending.
Open formats over lock-in
Iceberg, Parquet, Delta — your data is yours and portable across vendors.
Observability is not optional
Every system we build ships with metrics, logs and data-quality checks from day one.